Most current visualization systems generally suggest certainty. This means that when visualizations are displayed to users, they believe that what is currently displayed is absolute truth. However, there are many cases where this is not true. For example, several biological taxonomies and phylogenetic trees exist because not all biologists agree on one taxonomy or one phylogenetic tree and some analysis methods produce multiple possible trees. Some existing tree visualizations typically show one taxonomy at a time without any certainty information. Hence, there is no way to see which parts of the tree are certain or uncertain.
One common approach to comparing two tree structures is to use paired views side-by-side, using coupled interaction to allow users to compare and navigate two trees. This approach helps users identify where the differences are in two trees (usually by highlighting). However, this approach does not explicitly show the degree to which two parts are different.
To provide a complete and accurate visual representation of data, it is important to show uncertainty within the data. Uncertainty has been very broadly defined to include concepts such as error, inaccuracy/imprecision, minimum-maximum ranges, data quality, and missing data. For more than a decade, much research has described approaches to handling these various aspects of uncertainties. Geographic Visualization, Geographic Information System, and Scientific Visualization communities have given particular attention to uncertainty visualization and many techniques have been developed. The main techniques used to visualize uncertainty include adding glyphs, adding geometry, modifying geometry, modifying attributes, animation, sonification, and psycho-visual approach. While these techniques have been applied to a variety of applications such as fluid flow, surface interpolants, and volumetric rendering, only a few of them were actually evaluated. Furthermore, there has been little research on visualizing uncertainty in tree structures. One such study proposed visual representations to represent uncertainty in parent-child relationship in structures. For example, for node-link diagrams this study used blurred or dotted links to indicate less certain relationships. However, this study did not describe how to represent the degree of uncertainty.
An error can be defined as a difference between a computed, estimated, or measured value and the true or correct value. There are many cases where correct values are unknown but can be estimated using different techniques or algorithms. It is common to use the differences between two results as an error (or uncertainty). In fact, side-by-side comparison is one of the most commonly applied existing uncertainty visualization methods. Therefore, theoretically these kinds of visualization tools can be used to show uncertainty in tree structures.
In the biology domain, there exist several biological taxonomies and phylogenetic trees because not all biologists agree on one taxonomy and one phylogenetic tree. In order to assess the quality of taxonomies and phylogenetic trees, it is important to understand which parts of two trees agree or disagree. One of the commonly used approaches to comparing two trees is to use paired views side-by-side, using coupled interaction. For example, one approach automatically matches nodes in two trees based on the shared ancestors, and highlights the differences locations. Another approach transforms a tree into a tabular representation, in which each leaf is represented as a column and the path from the root is stored in the attributes (rows). It displays both trees (in a tabular form) side-by-side and marks the cells of differences. Some visualizations provide a merged tree by combining two trees into one. One such visualization shows the merged tree on left, first tree at center, and second tree on right. It uses multiple tables to provide taxonomic names that are common or different. Another visualization also provides a single overview of the merged tree with the indication of difference, and uses matched twin detail windows to show similarities and differences via a zoomable interface. However, while these tools show the location of the differences, they fail to show the magnitude of the differences.